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05.12.03 Bioinformatics : Gene Expression Data Analysis Aidong Zhang Professor Computer Science and Engineering University at Buffalo University at Buffalo The State University of New York What is Bioinformatics Broad Definition The study of how information technologies are used to solve problems in biology Narrow Definition The creation and management of biological databases in support of genomic sequences Oxford English Dictionary (proposed) Conceptualizing biology in terms of molecules and applying information techniques to understand and organize the information associated with these molecules, on a large scale University at Buffalo The State University of New York Aims of Bioinformatics Simplest Organize data in a way that allows researchers to access information and submit new entries as they are produced Higher Develop tools and resources that aid in the analysis of data Advanced Use these tools to analyze the data and interpret the results in a biologically meaning manner University at Buffalo The State University of New York Subjects of Bioinfromatics Data Source Data Size Topics Raw DNA sequence 8.2 million sequences (9.5 billion bases) Separating regions Gene product prediction Protein sequence 300,000 sequences (~300 amino acids each) Sequence comparison, alignments, identification Macromolecular structure 13,000 structures (~1,000 atomic coordinates each) Structure prediction, 3D alignment Protein geometry measurements Genomes 40 complete genomes (1.6 million – 3 billion bases each) Molecular simulations Phylogenetic analysis Genomic-scale censuses Linkage analysis Gene expression ~20 time point measurements for ~6,000 genes Clustering, correlating patterns, mapping data to sequence, structural and biochemical data Literature 11 million citations Digital libraries Knowledge databases Metabolic pathways University at Buffalo The State University of New York Pathway simulations Figure taken from http://www.oml.gov/hgmis University at Buffalo The State University of New York DNA Microarray Experiments http://www.ipam.ucla.edu/programs/fg2000/fgt_speed7.ppt University at Buffalo The State University of New York Gene Expression Data Gene Expression Data Matrix • Each row represents a gene Gi ; • Each column represents an experiment condition Sj ; • Each cell Xij is a real value representing the gene expression level of gene Gi under condition Sj; • Xij > 0: over expressed • Xij < 0: under expressed • A time-series gene expression data matrix typically contains O(103) genes and O(10) time points. University at Buffalo The State University of New York Gene Expression Data genes sample 1 sample 2 X11 X12 X13 X21 X22 X23 X31 X32 X33 samples sample 3 • asymmetric dimensionality • 10 ~ 100 sample / condition • 1000 ~ 10000 gene • two-way analysis • sample space • gene space University at Buffalo The State University of New York Microarray Data Analysis • Analysis from two angles • sample as object, gene as attribute • gene as object, sample/condition as attribute University at Buffalo The State University of New York Challenges of Gene Data Analysis (1) Gene space: Automatically identify clusters of genes which express similar patterns in the data set Robust to huge amount of noise Effective to handle the highly intersected clusters Potential to visualize the clustering results University at Buffalo The State University of New York Co-expressed Genes Gene Expression Data Matrix Gene Expression Patterns Co-expressed Genes Why looking for co-expressed genes? Co-expression indicates co-function; Co-expression also indicates co-regulation. University at Buffalo The State University of New York Challenges of Gene Data Analysis (2) Sample space: unsupervised sample clustering presents interesting but also very challenging problems –The sample space and gene space are of very different dimensionality (101 ~ 102 samples versus 103 ~104 genes). –High percentage of irrelevant or redundant genes. –People usually have little knowledge about how to construct an informative gene space. University at Buffalo The State University of New York Sample Clustering Gene expression data clustering University at Buffalo The State University of New York Microarray Data Analysis Microaray Data Microarray Images Gene Expression Matrices Important Important patterns Important patterns patterns Sample Clusters Gene Expression Data Analysis Visualization Gene Expression Patterns University at Buffalo The State University of New York Our Approaches Density-based approach: recognizes a dense area as a cluster, and organizes the cluster structure of a data set into a hierarchical tree. caculate the density of each data object based on its neighboring data distribution. construct the "attraction" relationship between data objects according to object density. organize the attraction relationship into the "attraction tree". summarize the attraction tree by a hierarchical "density tree". derive clusters from density tree. University at Buffalo The State University of New York Our Approaches (2) Interrelated dimensional clustering -automatically perform two tasks: detection of meaningful sample patterns selection of those significant genes of empirical pattern University at Buffalo The State University of New York Our Approaches (3) Visualization tool: offers insightful information Detects the structure of dataset Three Aspects Explorative Confirmative Representative Microarray Analysis Status Numerical methods dominant Visualization serve graphical presentations of major clustering methods Visualization applied Global visualization (TreeView) Sammon’s mapping University at Buffalo The State University of New York TreeView VizStruct Architecture Explorative Visualization – Sample space Confirmative Visualization – Gene space University at Buffalo The State University of New York VizStruct - Dimension Tour Interactively adjust dimension parameters Manually or automatically May cause false clusters to break Create dynamic visualization University at Buffalo The State University of New York Visualized Results for a Time Series Data Set University at Buffalo The State University of New York Elements of Clustering Feature Selection. Select properly the features on which clustering is to be performed. Clustering Algorithm. Criteria (e.g. object function) Proximity Measure (e.g. Euclidean distance, Pearson correlation coefficient ) Cluster Validation. The assessment of clustering results. Interpretation of the results. University at Buffalo The State University of New York Supervised Analysis Select training samples (hold out…) Sort genes (t-test, ranking…) Select informative genes (top 50 ~ 200) Cluster or classification based on informative genes Class 1 Class 2 g1 1 1 … 1 0 0 … 0 g2 1 1 … 1 0 0 … 0 . . . . . . . g4131 0 0 … 0 1 1 … 1 g4132 0 0 … 0 1 1 … 1 University at Buffalo The State University of New York g1 1 1 … 1 0 0 … 0 g2 1 1 … 1 0 0 … 0 . . . g4131 0 0 … 0 1 1 … 1 g4132 0 0 … 0 1 1 … 1 Unsupervised Analysis Microarray data analysis methods can be divided into two categories: supervised/unsupervised analysis. We will focus on unsupervised sample classification which assume no membership information being assigned to any sample. Since the initial biological identification of sample classes has been slow, typically evolving through years of hypothesis-driven research, automatically discovering sample pattern presents a significant contribution in microarray data analysis. Unsupervised sample classification is much more complex than supervised manner. Many mature statistic methods such as t-test, Z-score, and Markov filter can not be applied without the phenotypes of samples known in advance. University at Buffalo The State University of New York Problem Statement Given a data matrix M in which the number of samples and the volume of genes are in different order of magnitude (|G|>>| S|) and the number of sample categories K. The goal is to find K mutually exclusive groups of the samples matching their empirical types, thus to discover their meaningful pattern and to find the set of genes which manifests the meaningful pattern. University at Buffalo The State University of New York Problem Statement samples Informative Genes 1 2 3 gene1 gene2 gene3 gene4 gene5 Noninformative Genes gene6 gene7 gene8 University at Buffalo The State University of New York 4 5 6 7 Problem Statement (2) samples Informative Genes 1 2 3 4 5 6 7 8 9 10 gene1 gene2 gene3 gene4 Noninformative Genes gene5 gene6 gene7 University at Buffalo The State University of New York Problem Statement (3) Class 1 Class 2 Class3 Class 1 genea geneb genec gened genee genef University at Buffalo The State University of New York Class 2 Class3 Related Work New tools using traditional methods : TreeView CLUTO CIT • SOM • K-means CNIO • Hierarchical clustering GeneSpring • Graph based clustering J-Express • PCA CLUSFAVOR Their similarity measures based on full gene space are interfered by high percentage of noise. University at Buffalo The State University of New York Related Work (2) Clustering with feature selection: (CLIFF, leaf ordering, two-way ordering) 1. Filtering the invarient genes • Bayes model • Rank variance • PCA 2. Partition the samples • Ncut • Min-Max Cut 3. Pruning genes based on the partition • Markov blanket filter • T-test • Leaf ordering University at Buffalo The State University of New York Related Work (3) Subspace clustering : Bi-clustering δ-clustering University at Buffalo The State University of New York Intra-pattern-steadiness We require each genes show either all “on” or all “off” within each sample class. Variance of a single gene: Var (i, y ) 1 S y 1 (w i, j jS y wi , S y ) 2 Average row variance: R( x, y ) 1 Gx Var (i, y) iG x 1 Gx S y 1 2 ( w w ) i , j i ,S y . iG x jS y University at Buffalo The State University of New York Intra-pattern-consistency(2) University at Buffalo The State University of New York Measure- Data(A) ment Data(B) residue 0.1975 0.4506 MSR 0.0494 0.4012 ARV* 339.0667 5.3000 Inter-pattern-divergence In our model, both ``inter-patternsteadiness'' and ``intrapattern-dissimilarity'‘ on the same gene are reflected. Average block distance: D ( x, ( y, y ' )) w iG x i,S y University at Buffalo The State University of New York wi , S Gx y' Pattern Quality The purpose of pattern discovery is to identify the empirical pattern where the patterns inside each class are steady and the divergence between each pair of classes is large. S y1 , S y2 1 R ( x, y1 ) R ( x, y2 ) D ( x, ( y1 , y2 )) University at Buffalo The State University of New York Pattern Quality (2) Data(A) Data(B) Data(C) Con 4.25 3.44 4.52 Div 41.60 25.20 46.16 14.2687 9.6074 University at Buffalo The State University of New York 15.3526 The Problem Input 1. m samples each measured by n-dimensional genes 2. the number of sample categories K Output A K partition of samples (empirical pattern) and a subset of genes (informative space) that the pattern quality of the partition projected on the gene subset reaches the highest. University at Buffalo The State University of New York Strategy Starts with a random K-partition of samples and a subset of genes as the candidate of the informative space. Iteratively adjust the partition and the gene set toward the optimal solution. Basic elements: A state: A partition of samples {S1,S2,…Sk} A set of genes G’G The corresponding pattern quality An adjustment For a gene For a gene For a sample G’, insert into G’ G’, remove from G’ in group S’, move to other group gi gi si University at Buffalo The State University of New York Strategy (2) Iteratively adjust the partition and the gene set toward the optimal pattern. for each gene, try possible insert/remove for each sample, try best movement. University at Buffalo The State University of New York Improvement Data Standardization o the original gene intensity values relative values ' i, j w wi , j wi i j 1 wi, j , where wi m 2 ( w w ) j 1 i, j i m m ; i m 1 Random order Conduct negative action with a probability Stimulated annealing p exp( ) T (i ) 1 T (0) 1; T (i ) . 1 i University at Buffalo The State University of New York Experimental Results Data Sets: Multiple-sclerosis data MS-IFN : 4132 * 28 (14 MS vs. 14 IFN) MS-CON : 4132 * 30 (15 MS vs. 15 Control) Leukemia data 7129 * 38 (27 ALL vs. 11 AML) 7129 * 34 (20 ALL vs. 14 AML) Colon Cancer data 2000 * 62 (22 normal vs. 40 tumor colon tissue) Hereditary breast cancer data 3226 * 22 ( 7 BRCA1, 8 BRCA2, 7 Sporadics) University at Buffalo The State University of New York Experimental Results (2) Multiple-sclerosis data 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000 CNIO CIT CLUSFAVO R Cluto J-Express Delta EPD* MS_IFN 0.4815 0.4841 0.5238 0.4815 0.4815 0.4894 0.8052 MS_CON 0.4920 0.4851 0.5402 0.4828 0.4851 0.4851 0.6230 University at Buffalo The State University of New York Interrelated Dimensional Clustering The approach is applied on classifying multiple-sclerosis patients and IFN-drug treated patients. (A) Shows the original 28 samples' distribution. Each point represents a sample, which is a mapping from the sample's 4132 genes intensity vectors. (B) Shows 28 samples' distribution on 2015 genes. (C) Shows 28 samples' distribution on 312 genes. (D) Shows the same 28 samples distribution after using our approach. We reduce 4132 genes to 96 genes. University at Buffalo The State University of New York Experimental Results Results (3) Experimental (3) Leukemia data 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000 CNIO CIT CLUSFAV OR Cluto J-Express Delta EPD* G1 0.6017 0.6586 0.5092 0.5775 0.5092 0.5007 0.9761 G2 0.4920 0.4920 0.4920 0.4866 0.4965 0.4538 0.7086 University at Buffalo The State University of New York Experimental Results Results (4) Experimental (4) Colon & Breast data 1.0000 0.8000 0.6000 0.4000 0.2000 0.0000 CNIO CIT CLUSFAVO R Cluto J-Express Delta EPD* Colon 0.4939 0.5844 0.5844 0.5974 0.4415 0.4796 0.6293 Brest 0.4112 0.5844 0.5844 0.6364 0.4112 0.4719 0.8638 University at Buffalo The State University of New York Applications Gene Function Co-expressed genes in the same cluster tend to share common roles in cellular processes and genes of unrelated sequence but similar function cluster tightly together. Similar tendency was observed in both yeast data and human data. Gene Regulation By searching for common DNA sequences at the promoter regions of genes within the same cluster, regulatory motifs specific to each gene cluster are identified. Cancer Prediction Normal vs. Tumor Tissue Classification Drug Treatment Evaluation … University at Buffalo The State University of New York Summary We have developed advanced approaches for gene expression data analysis which work more effectively than traditional analysis approaches This research area is exciting and challenging. There are a lot of interesting research issues. University at Buffalo The State University of New York